A Simple Tutorial on Exploratory Data Analysis¶

What is Exploratory Data Analysis (EDA)?¶

  • How to ensure you are ready to use machine learning algorithms in a project?
  • How to choose the most suitable algorithms for your data set?
  • How to define the feature variables that can potentially be used for machine learning?

Exploratory Data Analysis (EDA) helps to answer all these questions, ensuring the best outcomes for the project. It is an approach for summarizing, visualizing, and understanding the important characteristics of a data set.

Value of Exploratory Data Analysis¶

Exploratory Data Analysis is valuable to data science projects since it allows to get closer to the certainty that the future results will be valid, correctly interpreted, and applicable to the desired business contexts. EDA also helps to find insights that were not worth investigating to business stakeholders and data scientists but can be very informative about a particular business.

Methods of Exploratory Data Analysis¶

It is always better to explore each data set using multiple exploratory techniques and compare the results. The goal of this step is to become confident that the data set is ready to be used in a machine learning algorithm.

Exploratory Data Analysis is majorly performed using the following methods:

  • Univariate visualization — provides summary statistics for each field in the raw data set
  • Bivariate visualization — is performed to find the relationship between each variable in the dataset and the target variable of interest
  • Multivariate visualization — is performed to understand interactions between different fields in the dataset
  • Dimensionality reduction — helps to understand the fields in the data that account for the most variance between observations and allow for the processing of a reduced volume of data.

These methods help data scientist to identify the patterns and understand the problem.

Why skipping Exploratory Data Analysis is a bad idea?¶

In a hurry to get to the machine learning stage or simply impress business stakeholders very fast, data scientists tend to either entirely skip the exploratory process or do a very shallow work. It is a very serious and, sadly, common mistake of amateur data science consulting “professionals”.

Such inconsiderate behavior can lead to skewed data, with outliers and too many missing values and, therefore, some sad outcomes for the project:

  • generating inaccurate models;
  • generating accurate models on the wrong data;
  • choosing the wrong variables for the model;
  • inefficient use of the resources, including the rebuilding of the model.

Exploratory Data Analysis (EDA) is used on the one hand to answer questions, test business assumptions, generate hypotheses for further analysis. On the other hand, you can also use it to prepare the data for modeling.

The thing that these two probably have in common is a good knowledge of your data to either get the answers that you need or to develop an intuition for interpreting the results of future modeling.

There are a lot of ways to reach these goals as follows:

  1. Import the data

  2. Get a feel of the data ,describe the data,look at a sample of data like first and last rows

  3. Take a deeper look into the data by querying or indexing the data

  4. Identify features of interest

  5. Recognise the challenges posed by data - missing values, outliers

  6. Discover patterns in the data

One of the important things about EDA is Data profiling.

Data profiling is concerned with summarizing your dataset through descriptive statistics. You want to use a variety of measurements to better understand your dataset. The goal of data profiling is to have a solid understanding of your data so you can afterwards start querying and visualizing your data in various ways. However, this doesn’t mean that you don’t have to iterate: exactly because data profiling is concerned with summarizing your dataset, it is frequently used to assess the data quality. Depending on the result of the data profiling, you might decide to correct, discard or handle your data differently.

Key Concepts of Exploratory Data Analysis¶

  • 2 types of Data Analysis

    • Confirmatory Data Analysis

    • Exploratory Data Analysis

  • 4 Objectives of EDA

    • Discover Patterns

    • Spot Anomalies

    • Frame Hypothesis

    • Check Assumptions

  • 2 methods for exploration

    • Univariate Analysis

    • Bivariate Analysis

  • Stuff done during EDA

    • Trends

    • Distribution

    • Mean

    • Median

    • Outlier

    • Spread measurement (SD)

    • Correlations

    • Hypothesis testing

    • Visual Exploration

Overview¶

This is an exploratory data analysis on the House Prices Kaggle Competition found at

https://www.kaggle.com/c/house-prices-advanced-regression-techniques

Description¶

Ask a home buyer to describe their dream house, and they probably won't begin with the height of the basement ceiling or the proximity to an east-west railroad. But this playground competition's dataset proves that much more influences price negotiations than the number of bedrooms or a white-picket fence.

With 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa, this competition challenges you to predict the final price of each home.

There are 1460 instances of training data and 1460 of test data. Total number of attributes equals 81, of which 36 are numerical, 43 are categorical + Id and SalePrice.

Numerical Features: 1stFlrSF, 2ndFlrSF, 3SsnPorch, BedroomAbvGr, BsmtFinSF1, BsmtFinSF2, BsmtFullBath, BsmtHalfBath, BsmtUnfSF, EnclosedPorch, Fireplaces, FullBath, GarageArea, GarageCars, GarageYrBlt, GrLivArea, HalfBath, KitchenAbvGr, LotArea, LotFrontage, LowQualFinSF, MSSubClass, MasVnrArea, MiscVal, MoSold, OpenPorchSF, OverallCond, OverallQual, PoolArea, ScreenPorch, TotRmsAbvGrd, TotalBsmtSF, WoodDeckSF, YearBuilt, YearRemodAdd, YrSold

Categorical Features: Alley, BldgType, BsmtCond, BsmtExposure, BsmtFinType1, BsmtFinType2, BsmtQual, CentralAir, Condition1, Condition2, Electrical, ExterCond, ExterQual, Exterior1st, Exterior2nd, Fence, FireplaceQu, Foundation, Functional, GarageCond, GarageFinish, GarageQual, GarageType, Heating, HeatingQC, HouseStyle, KitchenQual, LandContour, LandSlope, LotConfig, LotShape, MSZoning, MasVnrType, MiscFeature, Neighborhood, PavedDrive, PoolQC, RoofMatl, RoofStyle, SaleCondition, SaleType, Street, Utilitif

Data fields¶

SalePrice - the property's sale price in dollars. This is the target variable that you're trying to predict.

MSSubClass: The building class

MSZoning: The general zoning classification

LotFrontage: Linear feet of street connected to property

LotArea: Lot size in square feet

Street: Type of road access

Alley: Type of alley access

LotShape: General shape of property

LandContour: Flatness of the property

Utilities: Type of utilities available

LotConfig: Lot configuration

LandSlope: Slope of property

Neighborhood: Physical locations within Ames city limits

Condition1: Proximity to main road or railroad

Condition2: Proximity to main road or railroad (if a second is present)

BldgType: Type of dwelling

HouseStyle: Style of dwelling

OverallQual: Overall material and finish quality

OverallCond: Overall condition rating

YearBuilt: Original construction date

YearRemodAdd: Remodel date

RoofStyle: Type of roof

RoofMatl: Roof material

Exterior1st: Exterior covering on house

Exterior2nd: Exterior covering on house (if more than one material)

MasVnrType: Masonry veneer type

MasVnrArea: Masonry veneer area in square feet

ExterQual: Exterior material quality

ExterCond: Present condition of the material on the exterior

Foundation: Type of foundation

BsmtQual: Height of the basement

BsmtCond: General condition of the basement

BsmtExposure: Walkout or garden level basement walls

BsmtFinType1: Quality of basement finished area

BsmtFinSF1: Type 1 finished square feet

BsmtFinType2: Quality of second finished area (if present)

BsmtFinSF2: Type 2 finished square feet

BsmtUnfSF: Unfinished square feet of basement area

TotalBsmtSF: Total square feet of basement area

Heating: Type of heating

HeatingQC: Heating quality and condition

CentralAir: Central air conditioning

Electrical: Electrical system

1stFlrSF: First Floor square feet

2ndFlrSF: Second floor square feet

LowQualFinSF: Low quality finished square feet (all floors)

GrLivArea: Above grade (ground) living area square feet

BsmtFullBath: Basement full bathrooms

BsmtHalfBath: Basement half bathrooms

FullBath: Full bathrooms above grade

HalfBath: Half baths above grade

Bedroom: Number of bedrooms above basement level

Kitchen: Number of kitchens

KitchenQual: Kitchen quality

TotRmsAbvGrd: Total rooms above grade (does not include bathrooms)

Functional: Home functionality rating

Fireplaces: Number of fireplaces

FireplaceQu: Fireplace quality

GarageType: Garage location

GarageYrBlt: Year garage was built

GarageFinish: Interior finish of the garage

GarageCars: Size of garage in car capacity

GarageArea: Size of garage in square feet

GarageQual: Garage quality

GarageCond: Garage condition

PavedDrive: Paved driveway

WoodDeckSF: Wood deck area in square feet

OpenPorchSF: Open porch area in square feet

EnclosedPorch: Enclosed porch area in square feet

3SsnPorch: Three season porch area in square feet

ScreenPorch: Screen porch area in square feet

PoolArea: Pool area in square feet

PoolQC: Pool quality

Fence: Fence quality

MiscFeature: Miscellaneous feature not covered in other categories

MiscVal: $Value of miscellaneous feature

MoSold: Month Sold

YrSold: Year Sold

SaleType: Type of sale

SaleCondition: Condition of sale

Loading Libraries¶

Load libraries that you think you will require or you can load as you go along.

In [5]:
!pip install missingno

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Installing collected packages: missingno
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In [1]:
import pandas as pd
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
import scipy.stats as st
from sklearn import ensemble, tree, linear_model
import missingno as msno

To start exploring your data, you’ll need to start by actually loading in your data. You’ll probably know this already, but thanks to the Pandas library, this becomes an easy task: you import the package as pd, following the convention, and you use the read_csv() function, to which you pass the URL in which the data can be found and a header argument. This last argument is one that you can use to make sure that your data is read in correctly: the first row of your data won’t be interpreted as the column names of your DataFrame.

Alternatively, there are also other arguments that you can specify to ensure that your data is read in correctly: you can specify the delimiter to use with the sep or delimiter arguments, the column names to use with names or the column to use as the row labels for the resulting DataFrame with index_col.

Dataset Download¶

train1: https://docs.google.com/spreadsheets/d/1vcyrotDfG08wccCRZ7fCaP5Ea-aEHbAPDDZjl1-q8vY/edit?usp=sharing

test: https://docs.google.com/spreadsheets/d/1ahW5jGOo_Hcy-K0BiRfUT_11ifPh92hhw73ZX9oHS4E/edit?usp=sharing

In [2]:
train = pd.read_csv('train1.csv')
test = pd.read_csv('test1.csv')

One of the most elementary steps to do this is by getting a basic description of your data. A basic description of your data is indeed a very broad term: you can interpret it as a quick and dirty way to get some information on your data, as a way of getting some simple, easy-to-understand information on your data, to get a basic feel for your data. We can use the describe() function to get various summary statistics that exclude NaN values.

In [5]:
train.describe()
Out[5]:
Id MSSubClass LotFrontage LotArea OverallQual OverallCond YearBuilt YearRemodAdd MasVnrArea BsmtFinSF1 ... WoodDeckSF OpenPorchSF EnclosedPorch 3SsnPorch ScreenPorch PoolArea MiscVal MoSold YrSold SalePrice
count 1460.000000 1460.000000 1201.000000 1460.000000 1460.000000 1460.000000 1460.000000 1460.000000 1452.000000 1460.000000 ... 1460.000000 1460.000000 1460.000000 1460.000000 1460.000000 1460.000000 1460.000000 1460.000000 1460.000000 1460.000000
mean 730.500000 56.897260 70.049958 10516.828082 6.099315 5.575342 1971.267808 1984.865753 103.685262 443.639726 ... 94.244521 46.660274 21.954110 3.409589 15.060959 2.758904 43.489041 6.321918 2007.815753 180921.195890
std 421.610009 42.300571 24.284752 9981.264932 1.382997 1.112799 30.202904 20.645407 181.066207 456.098091 ... 125.338794 66.256028 61.119149 29.317331 55.757415 40.177307 496.123024 2.703626 1.328095 79442.502883
min 1.000000 20.000000 21.000000 1300.000000 1.000000 1.000000 1872.000000 1950.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 2006.000000 34900.000000
25% 365.750000 20.000000 59.000000 7553.500000 5.000000 5.000000 1954.000000 1967.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 5.000000 2007.000000 129975.000000
50% 730.500000 50.000000 69.000000 9478.500000 6.000000 5.000000 1973.000000 1994.000000 0.000000 383.500000 ... 0.000000 25.000000 0.000000 0.000000 0.000000 0.000000 0.000000 6.000000 2008.000000 163000.000000
75% 1095.250000 70.000000 80.000000 11601.500000 7.000000 6.000000 2000.000000 2004.000000 166.000000 712.250000 ... 168.000000 68.000000 0.000000 0.000000 0.000000 0.000000 0.000000 8.000000 2009.000000 214000.000000
max 1460.000000 190.000000 313.000000 215245.000000 10.000000 9.000000 2010.000000 2010.000000 1600.000000 5644.000000 ... 857.000000 547.000000 552.000000 508.000000 480.000000 738.000000 15500.000000 12.000000 2010.000000 755000.000000

8 rows × 38 columns

Now that you have got a general idea about your data set, it’s also a good idea to take a closer look at the data itself. With the help of the head() and tail() functions of the Pandas library, you can easily check out the first and last lines of your DataFrame, respectively.

Let us look at some sample data

In [7]:
train.head()
Out[7]:
Id MSSubClass MSZoning LotFrontage LotArea Street Alley LotShape LandContour Utilities ... PoolArea PoolQC Fence MiscFeature MiscVal MoSold YrSold SaleType SaleCondition SalePrice
0 1 60 RL 65.0 8450 Pave NaN Reg Lvl AllPub ... 0 NaN NaN NaN 0 2 2008 WD Normal 208500
1 2 20 RL 80.0 9600 Pave NaN Reg Lvl AllPub ... 0 NaN NaN NaN 0 5 2007 WD Normal 181500
2 3 60 RL 68.0 11250 Pave NaN IR1 Lvl AllPub ... 0 NaN NaN NaN 0 9 2008 WD Normal 223500
3 4 70 RL 60.0 9550 Pave NaN IR1 Lvl AllPub ... 0 NaN NaN NaN 0 2 2006 WD Abnorml 140000
4 5 60 RL 84.0 14260 Pave NaN IR1 Lvl AllPub ... 0 NaN NaN NaN 0 12 2008 WD Normal 250000

5 rows × 81 columns

In [8]:
train.tail()
Out[8]:
Id MSSubClass MSZoning LotFrontage LotArea Street Alley LotShape LandContour Utilities ... PoolArea PoolQC Fence MiscFeature MiscVal MoSold YrSold SaleType SaleCondition SalePrice
1455 1456 60 RL 62.0 7917 Pave NaN Reg Lvl AllPub ... 0 NaN NaN NaN 0 8 2007 WD Normal 175000
1456 1457 20 RL 85.0 13175 Pave NaN Reg Lvl AllPub ... 0 NaN MnPrv NaN 0 2 2010 WD Normal 210000
1457 1458 70 RL 66.0 9042 Pave NaN Reg Lvl AllPub ... 0 NaN GdPrv Shed 2500 5 2010 WD Normal 266500
1458 1459 20 RL 68.0 9717 Pave NaN Reg Lvl AllPub ... 0 NaN NaN NaN 0 4 2010 WD Normal 142125
1459 1460 20 RL 75.0 9937 Pave NaN Reg Lvl AllPub ... 0 NaN NaN NaN 0 6 2008 WD Normal 147500

5 rows × 81 columns

In [9]:
train.shape , test.shape
Out[9]:
((1460, 81), (1459, 80))

Let us examine numerical features in the train dataset

In [7]:
numeric_features = train.select_dtypes(include=[np.number])
numeric_features.columns
Out[7]:
Index(['Id', 'MSSubClass', 'LotFrontage', 'LotArea', 'OverallQual',
       'OverallCond', 'YearBuilt', 'YearRemodAdd', 'MasVnrArea', 'BsmtFinSF1',
       'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', '1stFlrSF', '2ndFlrSF',
       'LowQualFinSF', 'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath',
       'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'TotRmsAbvGrd',
       'Fireplaces', 'GarageYrBlt', 'GarageCars', 'GarageArea', 'WoodDeckSF',
       'OpenPorchSF', 'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea',
       'MiscVal', 'MoSold', 'YrSold', 'SalePrice'],
      dtype='object')
In [8]:
numeric_features.head()
Out[8]:
Id MSSubClass LotFrontage LotArea OverallQual OverallCond YearBuilt YearRemodAdd MasVnrArea BsmtFinSF1 ... WoodDeckSF OpenPorchSF EnclosedPorch 3SsnPorch ScreenPorch PoolArea MiscVal MoSold YrSold SalePrice
0 1 60 65.0 8450 7 5 2003 2003 196.0 706 ... 0 61 0 0 0 0 0 2 2008 208500
1 2 20 80.0 9600 6 8 1976 1976 0.0 978 ... 298 0 0 0 0 0 0 5 2007 181500
2 3 60 68.0 11250 7 5 2001 2002 162.0 486 ... 0 42 0 0 0 0 0 9 2008 223500
3 4 70 60.0 9550 7 5 1915 1970 0.0 216 ... 0 35 272 0 0 0 0 2 2006 140000
4 5 60 84.0 14260 8 5 2000 2000 350.0 655 ... 192 84 0 0 0 0 0 12 2008 250000

5 rows × 38 columns

Temporal Variables(Eg: Datetime Variables)¶

From the Dataset we have 4 year variables. We have extract information from the datetime variables like no of years or no of days.

In [9]:
# list of variables that contain year information
year_feature = [feature for feature in numeric_features if 'Yr' in feature or 'Year' in feature]

year_feature
Out[9]:
['YearBuilt', 'YearRemodAdd', 'GarageYrBlt', 'YrSold']
In [10]:
# Let us explore the contents of temporal  variables
for feature in year_feature:
    print(feature, train[feature].unique())

YearBuilt [2003 1976 2001 1915 2000 1993 2004 1973 1931 1939 1965 2005 1962 2006
 1960 1929 1970 1967 1958 1930 2002 1968 2007 1951 1957 1927 1920 1966
 1959 1994 1954 1953 1955 1983 1975 1997 1934 1963 1981 1964 1999 1972
 1921 1945 1982 1998 1956 1948 1910 1995 1991 2009 1950 1961 1977 1985
 1979 1885 1919 1990 1969 1935 1988 1971 1952 1936 1923 1924 1984 1926
 1940 1941 1987 1986 2008 1908 1892 1916 1932 1918 1912 1947 1925 1900
 1980 1989 1992 1949 1880 1928 1978 1922 1996 2010 1946 1913 1937 1942
 1938 1974 1893 1914 1906 1890 1898 1904 1882 1875 1911 1917 1872 1905]
YearRemodAdd [2003 1976 2002 1970 2000 1995 2005 1973 1950 1965 2006 1962 2007 1960
 2001 1967 2004 2008 1997 1959 1990 1955 1983 1980 1966 1963 1987 1964
 1972 1996 1998 1989 1953 1956 1968 1981 1992 2009 1982 1961 1993 1999
 1985 1979 1977 1969 1958 1991 1971 1952 1975 2010 1984 1986 1994 1988
 1954 1957 1951 1978 1974]
GarageYrBlt [2003. 1976. 2001. 1998. 2000. 1993. 2004. 1973. 1931. 1939. 1965. 2005.
 1962. 2006. 1960. 1991. 1970. 1967. 1958. 1930. 2002. 1968. 2007. 2008.
 1957. 1920. 1966. 1959. 1995. 1954. 1953.   nan 1983. 1977. 1997. 1985.
 1963. 1981. 1964. 1999. 1935. 1990. 1945. 1987. 1989. 1915. 1956. 1948.
 1974. 2009. 1950. 1961. 1921. 1900. 1979. 1951. 1969. 1936. 1975. 1971.
 1923. 1984. 1926. 1955. 1986. 1988. 1916. 1932. 1972. 1918. 1980. 1924.
 1996. 1940. 1949. 1994. 1910. 1978. 1982. 1992. 1925. 1941. 2010. 1927.
 1947. 1937. 1942. 1938. 1952. 1928. 1922. 1934. 1906. 1914. 1946. 1908.
 1929. 1933.]
YrSold [2008 2007 2006 2009 2010]

We will compare the difference between All years feature with SalePrice

In [11]:
## Here we will compare the difference between All years feature with SalePrice
for feature in year_feature:
    if feature!='YrSold':
        data=train.copy()
        ## We will capture the difference between year variable and year the house was sold for
        data[feature]=data['YrSold']-data[feature]

        plt.scatter(data[feature],data['SalePrice'])
        plt.xlabel(feature)
        plt.ylabel('SalePrice')
        plt.show()
In [39]:
for feature in year_feature:
    if feature!='YrSold':
        data=train.copy()
        ## We will capture the difference between year variable and year the house was sold for
        data[feature]=data['YrSold']-data[feature]
        plt.scatter(data[feature],data['SalePrice'])
        plt.xlabel(feature, fontsize=12, fontfamily='serif')
        plt.ylabel('SalePrice', fontsize=12, fontfamily='serif')
        plt.title(f"{feature} versus SalePrice", fontsize=12, 
                fontfamily='serif', fontweight = 'bold')
        g = sns.despine(top=True, right=True)
        plt.tick_params(which=u'both', axis=u'both', length=0)
                        
        plt.show()

Numerical variables Types¶

1. Discrete Variables¶

Note: The below piece of code may not be relevant piece of code as it has very little relevance when we look at it from the perspective of building machine learning models. Sharing some info here, please let me know if you need some more clarity. Referring to "train[feature].unique())<25" - it collects features with unique less than 25 unique values. Otherwise it may not make sense to plot all the categorical features in place. For example: If we have 100 unique values in features, it may not be meaningful to draw insights from that variable via visualization. Now why is not meaningful or worth? - Basically, if we go ahead with doing it, you will be seeing 100 number of categories and there is hardly anything that we could infer from it even if we visualize, hence as a best practice people go with 25 or any other number that it is lesser or slightly more. This is done more for the convenience of visualization and has got no relevance in building a machine learning model.

In [21]:
discrete_feature=[feature for feature in numeric_features if len(train[feature].unique())<25 and feature not in year_feature + ['Id']]
print("Discrete Variables Count: {}".format(len(discrete_feature)))

""" 
Understanding the code above: This is list comprehension

If you have noticed some of the earlier cells (i.e. section Temporal variables),  year_feature is nothing but list of all the features/variables that 
contains 'Yr' or 'Year' in its name, for example: 'YearBuilt' and 'GarageYrBlt' are two among them.

numeric_features is a list of all the features which are numerical in nature (just above Temporal Variable section)
Now coming to code above discrete_features = ..............
- train[feature].unique() will return all the unique values the particular feature is containing.
- len(train[feature].unique()) will give you total number of elements in train[feature].unique() i.e. total number of unique values that feature has

Explaining the code:
for each feature in numeric feature it will check if that feature has unique values less than 25 and that feature is not included in year_feature+['Id'] 
(year_feature + ['Id'] means all the list of features in year_feature including 'Id'), and if both the conditions are satisfied then select that feature 
and this feature will be included in discrete feature.

Further codes explanation:
If you see below, there are 17 discrete variables which do not have 'Yr' or 'Year' in its name and has unique values less than 25.
You can see all the discrete features shown below.

Hope it helped you understanding the code. If not let me know.
"""

Discrete Variables Count: 17
Out[21]:
" \nUnderstanding the code above: This is list comprehension\n\nIf you have noticed some of the earlier cells (i.e. section Temporal variables),  year_feature is nothing but list of all the features/variables that \ncontains 'Yr' or 'Year' in its name, for example: 'YearBuilt' and 'GarageYrBlt' are two among them.\n\nnumeric_features is a list of all the features which are numerical in nature (just above Temporal Variable section)\nNow coming to code above discrete_features = ..............\n- train[feature].unique() will return all the unique values the particular feature is containing.\n- len(train[feature].unique()) will give you total number of elements in train[feature].unique() i.e. total number of unique values that feature has\n\nExplaining the code:\nfor each feature in numeric feature it will check if that feature has unique values less than 25 and that feature is not included in year_feature+['Id'] \n(year_feature + ['Id'] means all the list of features in year_feature including 'Id'), and if both the conditions are satisfied then select that feature \nand this feature will be included in discrete feature.\n\nFurther codes explanation:\nIf you see below, there are 17 discrete variables which do not have 'Yr' or 'Year' in its name and has unique values less than 25.\nYou can see all the discrete features shown below.\n\nHope it helped you understanding the code. If not let me know.\n"
In [22]:
train[discrete_feature].head()
Out[22]:
MSSubClass OverallQual OverallCond LowQualFinSF BsmtFullBath BsmtHalfBath FullBath HalfBath BedroomAbvGr KitchenAbvGr TotRmsAbvGrd Fireplaces GarageCars 3SsnPorch PoolArea MiscVal MoSold
0 60 7 5 0 1 0 2 1 3 1 8 0 2 0 0 0 2
1 20 6 8 0 0 1 2 0 3 1 6 1 2 0 0 0 5
2 60 7 5 0 1 0 2 1 3 1 6 1 2 0 0 0 9
3 70 7 5 0 1 0 1 0 3 1 7 1 3 0 0 0 2
4 60 8 5 0 1 0 2 1 4 1 9 1 3 0 0 0 12

Now let us find the relationship between these discrete features and Sale Price¶

In [38]:
for feature in discrete_feature:
    data=train.copy()
    data.groupby(feature)['SalePrice'].median().plot.bar()
    plt.xlabel(feature, fontsize=12, fontfamily='serif')
    plt.ylabel('SalePrice', fontsize=12, fontfamily='serif')
    plt.title(feature, fontsize=12, fontfamily='serif', fontweight = 'bold')
    g = sns.despine(top=True, left = True, right=True, bottom = True)
    plt.tick_params(which=u'both', axis=u'both', length=0)
        
    # Show the plot
    plt.show()

2. Continuous Variables:¶

In [41]:
continuous_feature=[feature for feature in numeric_features if feature not in discrete_feature+year_feature+['Id']]
print("Continuous Feature Count {}".format(len(continuous_feature)))

Continuous Feature Count 16

Let us analyse the continuous values with data visualisation to understand the data distribution

In [42]:
for feature in continuous_feature:
    data=train.copy()
    data[feature].hist(bins=25, grid = False)
    plt.xlabel(feature, fontsize = 12, fontfamily = 'serif')
    plt.ylabel("Count", fontsize = 12, fontfamily = 'serif')
    plt.title(feature, fontsize = 15, fontfamily = 'serif', fontweight = 'bold')
    
    g = sns.despine(top=True, left = True, right=True, bottom = True)
    plt.tick_params(which=u'both', axis=u'both', length=0)
    
    # Show the plot    
    plt.show()

Let us examine categorical features in the train dataset¶

In [44]:
categorical_features = train.select_dtypes(include=[object])
categorical_features.columns
Out[44]:
Index(['MSZoning', 'Street', 'Alley', 'LotShape', 'LandContour', 'Utilities',
       'LotConfig', 'LandSlope', 'Neighborhood', 'Condition1', 'Condition2',
       'BldgType', 'HouseStyle', 'RoofStyle', 'RoofMatl', 'Exterior1st',
       'Exterior2nd', 'MasVnrType', 'ExterQual', 'ExterCond', 'Foundation',
       'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2',
       'Heating', 'HeatingQC', 'CentralAir', 'Electrical', 'KitchenQual',
       'Functional', 'FireplaceQu', 'GarageType', 'GarageFinish', 'GarageQual',
       'GarageCond', 'PavedDrive', 'PoolQC', 'Fence', 'MiscFeature',
       'SaleType', 'SaleCondition'],
      dtype='object')

Visualising missing values for a sample of 250

In [46]:
msno.matrix(train.sample(250))
plt.show()

Heatmap¶

The missingno correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another:

In [47]:
msno.heatmap(train)
plt.show()
In [48]:
msno.bar(train.sample(1000))
plt.show()

Dendrogram¶

The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap:

In [49]:
msno.dendrogram(train)
plt.show()

The dendrogram uses a hierarchical clustering algorithm (courtesy of scipy) to bin variables against one another by their nullity correlation (measured in terms of binary distance). At each step of the tree the variables are split up based on which combination minimizes the distance of the remaining clusters. The more monotone the set of variables, the closer their total distance is to zero, and the closer their average distance (the y-axis) is to zero.

To interpret this graph, read it from a top-down perspective. Cluster leaves which linked together at a distance of zero fully predict one another's presence—one variable might always be empty when another is filled, or they might always both be filled or both empty, and so on. In this specific example the dendrogram glues together the variables which are required and therefore present in every record.

Cluster leaves which split close to zero, but not at it, predict one another very well, but still imperfectly. If your own interpretation of the dataset is that these columns actually are or ought to be match each other in nullity , then the height of the cluster leaf tells you, in absolute terms, how often the records are "mismatched" or incorrectly filed—that is, how many values you would have to fill in or drop, if you are so inclined.

As with matrix, only up to 50 labeled columns will comfortably display in this configuration. However the dendrogram more elegantly handles extremely large datasets by simply flipping to a horizontal configuration.

The Challenges of Your Data

Now that we have gathered some basic information on your data, it’s a good idea to just go a little bit deeper into the challenges that the data might pose.

There are two factors mostly observed in EDA exercise which are missing values and outliers For understanding in detail on how to handle missing values in detail please visit https://www.kaggle.com/pavansanagapati/simple-tutorial-on-how-to-handle-missing-data For determining the outliers boxplot is used in the later part of this kernel

Estimate Skewness and Kurtosis

In [50]:
train.skew(), train.kurt()

C:\Users\HP\AppData\Local\Temp\ipykernel_3896\833229632.py:1: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.
  train.skew(), train.kurt()
Out[50]:
(Id                0.000000
 MSSubClass        1.407657
 LotFrontage       2.163569
 LotArea          12.207688
 OverallQual       0.216944
 OverallCond       0.693067
 YearBuilt        -0.613461
 YearRemodAdd     -0.503562
 MasVnrArea        2.669084
 BsmtFinSF1        1.685503
 BsmtFinSF2        4.255261
 BsmtUnfSF         0.920268
 TotalBsmtSF       1.524255
 1stFlrSF          1.376757
 2ndFlrSF          0.813030
 LowQualFinSF      9.011341
 GrLivArea         1.366560
 BsmtFullBath      0.596067
 BsmtHalfBath      4.103403
 FullBath          0.036562
 HalfBath          0.675897
 BedroomAbvGr      0.211790
 KitchenAbvGr      4.488397
 TotRmsAbvGrd      0.676341
 Fireplaces        0.649565
 GarageYrBlt      -0.649415
 GarageCars       -0.342549
 GarageArea        0.179981
 WoodDeckSF        1.541376
 OpenPorchSF       2.364342
 EnclosedPorch     3.089872
 3SsnPorch        10.304342
 ScreenPorch       4.122214
 PoolArea         14.828374
 MiscVal          24.476794
 MoSold            0.212053
 YrSold            0.096269
 SalePrice         1.882876
 dtype: float64,
 Id                -1.200000
 MSSubClass         1.580188
 LotFrontage       17.452867
 LotArea          203.243271
 OverallQual        0.096293
 OverallCond        1.106413
 YearBuilt         -0.439552
 YearRemodAdd      -1.272245
 MasVnrArea        10.082417
 BsmtFinSF1        11.118236
 BsmtFinSF2        20.113338
 BsmtUnfSF          0.474994
 TotalBsmtSF       13.250483
 1stFlrSF           5.745841
 2ndFlrSF          -0.553464
 LowQualFinSF      83.234817
 GrLivArea          4.895121
 BsmtFullBath      -0.839098
 BsmtHalfBath      16.396642
 FullBath          -0.857043
 HalfBath          -1.076927
 BedroomAbvGr       2.230875
 KitchenAbvGr      21.532404
 TotRmsAbvGrd       0.880762
 Fireplaces        -0.217237
 GarageYrBlt       -0.418341
 GarageCars         0.220998
 GarageArea         0.917067
 WoodDeckSF         2.992951
 OpenPorchSF        8.490336
 EnclosedPorch     10.430766
 3SsnPorch        123.662379
 ScreenPorch       18.439068
 PoolArea         223.268499
 MiscVal          701.003342
 MoSold            -0.404109
 YrSold            -1.190601
 SalePrice          6.536282
 dtype: float64)
In [56]:
y = train['SalePrice']
plt.figure(1); plt.title('Johnson SU')
sns.distplot(y, kde=False, fit=st.johnsonsu)
plt.figure(2); plt.title('Normal')
sns.distplot(y, kde=False, fit=st.norm)
plt.figure(3); plt.title('Log Normal')
sns.distplot(y, kde=False, fit=st.lognorm)

C:\Users\HP\AppData\Local\Temp\ipykernel_3896\1021860808.py:3: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(y, kde=False, fit=st.johnsonsu)
C:\Users\HP\AppData\Local\Temp\ipykernel_3896\1021860808.py:5: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(y, kde=False, fit=st.norm)
C:\Users\HP\AppData\Local\Temp\ipykernel_3896\1021860808.py:7: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(y, kde=False, fit=st.lognorm)
Out[56]:
<AxesSubplot: title={'center': 'Log Normal'}, xlabel='SalePrice'>

It is apparent that SalePrice doesn't follow normal distribution, so before performing regression it has to be transformed. While log transformation does pretty good job, best fit is unbounded Johnson distribution.

In [58]:
sns.distplot(train.skew(),color='blue',axlabel ='Skewness')

C:\Users\HP\AppData\Local\Temp\ipykernel_3896\1860264502.py:1: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.
  sns.distplot(train.skew(),color='blue',axlabel ='Skewness')
C:\Users\HP\AppData\Local\Temp\ipykernel_3896\1860264502.py:1: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(train.skew(),color='blue',axlabel ='Skewness')
Out[58]:
<AxesSubplot: xlabel='Skewness', ylabel='Density'>
In [59]:
plt.figure(figsize = (12,8))
sns.distplot(train.kurt(),color='r',axlabel ='Kurtosis',norm_hist= False, kde = True,rug = False)
#plt.hist(train.kurt(),orientation = 'vertical',histtype = 'bar',label ='Kurtosis', color ='blue')
plt.show()

C:\Users\HP\AppData\Local\Temp\ipykernel_3896\2341891893.py:2: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.
  sns.distplot(train.kurt(),color='r',axlabel ='Kurtosis',norm_hist= False, kde = True,rug = False)
C:\Users\HP\AppData\Local\Temp\ipykernel_3896\2341891893.py:2: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(train.kurt(),color='r',axlabel ='Kurtosis',norm_hist= False, kde = True,rug = False)
In [60]:
plt.hist(train['SalePrice'],orientation = 'vertical',histtype = 'bar', color ='blue')
plt.show()
In [33]:
target = np.log(train['SalePrice'])
target.skew()
plt.hist(target,color='blue')
Out[33]:
(array([  5.,  12.,  54., 184., 470., 400., 220.,  90.,  19.,   6.]),
 array([10.46024211, 10.7676652 , 11.07508829, 11.38251138, 11.68993448,
        11.99735757, 12.30478066, 12.61220375, 12.91962684, 13.22704994,
        13.53447303]),
 <a list of 10 Patch objects>)

Finding Correlation coefficients between numeric features and SalePrice

In [61]:
correlation = numeric_features.corr()
print(correlation['SalePrice'].sort_values(ascending = False),'\n')

SalePrice        1.000000
OverallQual      0.790982
GrLivArea        0.708624
GarageCars       0.640409
GarageArea       0.623431
TotalBsmtSF      0.613581
1stFlrSF         0.605852
FullBath         0.560664
TotRmsAbvGrd     0.533723
YearBuilt        0.522897
YearRemodAdd     0.507101
GarageYrBlt      0.486362
MasVnrArea       0.477493
Fireplaces       0.466929
BsmtFinSF1       0.386420
LotFrontage      0.351799
WoodDeckSF       0.324413
2ndFlrSF         0.319334
OpenPorchSF      0.315856
HalfBath         0.284108
LotArea          0.263843
BsmtFullBath     0.227122
BsmtUnfSF        0.214479
BedroomAbvGr     0.168213
ScreenPorch      0.111447
PoolArea         0.092404
MoSold           0.046432
3SsnPorch        0.044584
BsmtFinSF2      -0.011378
BsmtHalfBath    -0.016844
MiscVal         -0.021190
Id              -0.021917
LowQualFinSF    -0.025606
YrSold          -0.028923
OverallCond     -0.077856
MSSubClass      -0.084284
EnclosedPorch   -0.128578
KitchenAbvGr    -0.135907
Name: SalePrice, dtype: float64

To explore further we will start with the following visualisation methods to analyze the data better:

  • Correlation Heat Map
  • Zoomed Heat Map
  • Pair Plot
  • Scatter Plot

Correlation Heat Map¶

In [62]:
f , ax = plt.subplots(figsize = (14,12))
plt.title('Correlation of Numeric Features with Sale Price',y=1,size=16)
sns.heatmap(correlation,square = True,  vmax=0.8)
Out[62]:
<AxesSubplot: title={'center': 'Correlation of Numeric Features with Sale Price'}>

The heatmap is the best way to get a quick overview of correlated features thanks to seaborn!

At initial glance it is observed that there are two red colored squares that get my attention.

  1. The first one refers to the 'TotalBsmtSF' and '1stFlrSF' variables.
  2. Second one refers to the 'GarageX' variables. Both cases show how significant the correlation is between these variables. Actually, this correlation is so strong that it can indicate a situation of multicollinearity. If we think about these variables, we can conclude that they give almost the same information so multicollinearity really occurs.

Heatmaps are great to detect this kind of multicollinearity situations and in problems related to feature selection like this project, it comes as an excellent exploratory tool.

Another aspect I observed here is the 'SalePrice' correlations.As it is observed that 'GrLivArea', 'TotalBsmtSF', and 'OverallQual' saying a big 'Hello !' to SalePrice, however we cannot exclude the fact that rest of the features have some level of correlation to the SalePrice. To observe this correlation closer let us see it in Zoomed Heat Map

Zoomed HeatMap¶

SalePrice Correlation matrix¶

In [63]:
k= 11
cols = correlation.nlargest(k,'SalePrice')['SalePrice'].index
print(cols)
cm = np.corrcoef(train[cols].values.T)
f , ax = plt.subplots(figsize = (14,12))
sns.heatmap(cm, vmax=.8, linewidths=0.01,square=True,annot=True,cmap='viridis',
            linecolor="white",xticklabels = cols.values ,annot_kws = {'size':12},yticklabels = cols.values)

Index(['SalePrice', 'OverallQual', 'GrLivArea', 'GarageCars', 'GarageArea',
       'TotalBsmtSF', '1stFlrSF', 'FullBath', 'TotRmsAbvGrd', 'YearBuilt',
       'YearRemodAdd'],
      dtype='object')
Out[63]:
<AxesSubplot: >

From above zoomed heatmap it is observed that GarageCars & GarageArea are closely correlated . Similarly TotalBsmtSF and 1stFlrSF are also closely correlated.

My observations :

  • 'OverallQual', 'GrLivArea' and 'TotalBsmtSF' are strongly correlated with 'SalePrice'.
  • 'GarageCars' and 'GarageArea' are strongly correlated variables. It is because the number of cars that fit into the garage is a consequence of the garage area. 'GarageCars' and 'GarageArea' are like twin brothers. So it is hard to distinguish between the two. Therefore, we just need one of these variables in our analysis (we can keep 'GarageCars' since its correlation with 'SalePrice' is higher).
  • 'TotalBsmtSF' and '1stFloor' also seem to be twins. In this case let us keep 'TotalBsmtSF'
  • 'TotRmsAbvGrd' and 'GrLivArea', twins
  • 'YearBuilt' it appears like is slightly correlated with 'SalePrice'. This required more analysis to arrive at a conclusion may be do some time series analysis.

Pair Plot¶

Pair Plot between 'SalePrice' and correlated variables¶

Visualisation of 'OverallQual','TotalBsmtSF','GrLivArea','GarageArea','FullBath','YearBuilt','YearRemodAdd' features with respect to SalePrice in the form of pair plot & scatter pair plot for better understanding.

In [37]:
sns.set()
columns = ['SalePrice','OverallQual','TotalBsmtSF','GrLivArea','GarageArea','FullBath','YearBuilt','YearRemodAdd']
sns.pairplot(train[columns],size = 2 ,kind ='scatter',diag_kind='kde')
plt.show()

/usr/local/lib/python3.7/dist-packages/seaborn/axisgrid.py:2076: UserWarning: The `size` parameter has been renamed to `height`; please update your code.
  warnings.warn(msg, UserWarning)

Although we already know some of the main figures, this pair plot gives us a reasonable overview insight about the correlated features .Here are some of my analysis.

  • One interesting observation is between 'TotalBsmtSF' and 'GrLiveArea'. In this figure we can see the dots drawing a linear line, which almost acts like a border. It totally makes sense that the majority of the dots stay below that line. Basement areas can be equal to the above ground living area, but it is not expected a basement area bigger than the above ground living area.

  • One more interesting observation is between 'SalePrice' and 'YearBuilt'. In the bottom of the 'dots cloud', we see what almost appears to be a exponential function.We can also see this same tendency in the upper limit of the 'dots cloud'

  • Last observation is that prices are increasing faster now with respect to previous years.

Scatter Plot¶

Scatter plots between the most correlated variables¶

In [38]:
fig, ((ax1, ax2), (ax3, ax4),(ax5,ax6)) = plt.subplots(nrows=3, ncols=2, figsize=(14,10))
OverallQual_scatter_plot = pd.concat([train['SalePrice'],train['OverallQual']],axis = 1)
sns.regplot(x='OverallQual',y = 'SalePrice',data = OverallQual_scatter_plot,scatter= True, fit_reg=True, ax=ax1)
TotalBsmtSF_scatter_plot = pd.concat([train['SalePrice'],train['TotalBsmtSF']],axis = 1)
sns.regplot(x='TotalBsmtSF',y = 'SalePrice',data = TotalBsmtSF_scatter_plot,scatter= True, fit_reg=True, ax=ax2)
GrLivArea_scatter_plot = pd.concat([train['SalePrice'],train['GrLivArea']],axis = 1)
sns.regplot(x='GrLivArea',y = 'SalePrice',data = GrLivArea_scatter_plot,scatter= True, fit_reg=True, ax=ax3)
GarageArea_scatter_plot = pd.concat([train['SalePrice'],train['GarageArea']],axis = 1)
sns.regplot(x='GarageArea',y = 'SalePrice',data = GarageArea_scatter_plot,scatter= True, fit_reg=True, ax=ax4)
FullBath_scatter_plot = pd.concat([train['SalePrice'],train['FullBath']],axis = 1)
sns.regplot(x='FullBath',y = 'SalePrice',data = FullBath_scatter_plot,scatter= True, fit_reg=True, ax=ax5)
YearBuilt_scatter_plot = pd.concat([train['SalePrice'],train['YearBuilt']],axis = 1)
sns.regplot(x='YearBuilt',y = 'SalePrice',data = YearBuilt_scatter_plot,scatter= True, fit_reg=True, ax=ax6)
YearRemodAdd_scatter_plot = pd.concat([train['SalePrice'],train['YearRemodAdd']],axis = 1)
YearRemodAdd_scatter_plot.plot.scatter('YearRemodAdd','SalePrice')

*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.
Out[38]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fb269d90f50>
In [64]:
saleprice_overall_quality= train.pivot_table(index ='OverallQual',values = 'SalePrice', aggfunc = np.median)
saleprice_overall_quality.plot(kind = 'bar',color = 'blue')
plt.xlabel('Overall Quality')
plt.ylabel('Median Sale Price')
plt.show()

Box plot - OverallQual¶

In [72]:
var = 'OverallQual'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
f, ax = plt.subplots(figsize=(12, 8))
fig = sns.boxplot(x=var, y="SalePrice", data=data,
                 showmeans = True,meanline=True,
                  palette="viridis",
                 meanprops = {'linewidth':2, 'color':'red'})
fig.axis(ymin=0, ymax=800000);

Box plot - Neighborhood¶

In [41]:
var = 'Neighborhood'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
f, ax = plt.subplots(figsize=(16, 10))
fig = sns.boxplot(x=var, y="SalePrice", data=data)
fig.axis(ymin=0, ymax=800000);
xt = plt.xticks(rotation=45)

Count Plot - Neighborhood¶

In [42]:
plt.figure(figsize = (12, 6))
sns.countplot(x = 'Neighborhood', data = data)
xt = plt.xticks(rotation=45)

Based on the above observation can group those Neighborhoods with similar housing price into a same bucket for dimension-reduction.Let us see this in the preprocessing stage

With qualitative variables we can check distribution of SalePrice with respect to variable values and enumerate them.

In [73]:
for c in categorical_features:
    train[c] = train[c].astype('category')
    if train[c].isnull().any():
        train[c] = train[c].cat.add_categories(['MISSING'])
        train[c] = train[c].fillna('MISSING')

def boxplot(x, y, **kwargs):
    sns.boxplot(x=x, y=y)
    x=plt.xticks(rotation=90)
f = pd.melt(train, id_vars=['SalePrice'], value_vars=categorical_features)
g = sns.FacetGrid(f, col="variable",  col_wrap=2, sharex=False, sharey=False, height=5)
g = g.map(boxplot, "value", "SalePrice")

Housing Price vs Sales¶

  • Sale Type & Condition
  • Sales Seasonality
In [74]:
var = 'SaleType'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
f, ax = plt.subplots(figsize=(16, 10))
fig = sns.boxplot(x=var, y="SalePrice", data=data)
fig.axis(ymin=0, ymax=800000);
xt = plt.xticks(rotation=45)
In [45]:
var = 'SaleCondition'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
f, ax = plt.subplots(figsize=(16, 10))
fig = sns.boxplot(x=var, y="SalePrice", data=data)
fig.axis(ymin=0, ymax=800000);
xt = plt.xticks(rotation=45)

ViolinPlot - Functional vs.SalePrice¶

In [75]:
sns.violinplot(x = 'Functional', y = 'SalePrice', data = train);

FactorPlot - FirePlaceQC vs. SalePrice¶

In [76]:
sns.factorplot('FireplaceQu', 'SalePrice', data = train, color = 'm', \
               estimator = np.median, order = ['Ex', 'Gd', 'TA', 'Fa', 'Po'], height = 4.5,  aspect=1.35)

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
Cell In[76], line 1
----> 1 sns.factorplot('FireplaceQu', 'SalePrice', data = train, color = 'm', \
      2                estimator = np.median, order = ['Ex', 'Gd', 'TA', 'Fa', 'Po'], height = 4.5,  aspect=1.35)

AttributeError: module 'seaborn' has no attribute 'factorplot'

Facet Grid Plot - FirePlace QC vs.SalePrice¶

In [48]:
g = sns.FacetGrid(train, col = 'FireplaceQu', col_wrap = 3, col_order=['Ex', 'Gd', 'TA', 'Fa', 'Po'])
g.map(sns.boxplot, 'Fireplaces', 'SalePrice', order = [1, 2, 3], palette = 'Set2')
Out[48]:
<seaborn.axisgrid.FacetGrid at 0x7fb2685eee90>

PointPlot¶

In [49]:
plt.figure(figsize=(8,10))
g1 = sns.pointplot(x='Neighborhood', y='SalePrice', 
                   data=train, hue='LotShape')
g1.set_xticklabels(g1.get_xticklabels(),rotation=90)
g1.set_title("Lotshape Based on Neighborhood", fontsize=15)
g1.set_xlabel("Neighborhood")
g1.set_ylabel("Sale Price", fontsize=12)
plt.show()

Missing Value Analysis¶

We will first check the percentage of missing values present in each feature

In [52]:
data = pd.read_csv("train1.csv")
features_with_na=[features for features in data.columns if data[features].isnull().sum()>1]
for feature in features_with_na:
    print(feature, np.round(data[feature].isnull().mean(), 4),  ' % of Missing Values')

LotFrontage 0.1774  % of Missing Values
Alley 0.9377  % of Missing Values
MasVnrType 0.0055  % of Missing Values
MasVnrArea 0.0055  % of Missing Values
BsmtQual 0.0253  % of Missing Values
BsmtCond 0.0253  % of Missing Values
BsmtExposure 0.026  % of Missing Values
BsmtFinType1 0.0253  % of Missing Values
BsmtFinType2 0.026  % of Missing Values
FireplaceQu 0.4726  % of Missing Values
GarageType 0.0555  % of Missing Values
GarageYrBlt 0.0555  % of Missing Values
GarageFinish 0.0555  % of Missing Values
GarageQual 0.0555  % of Missing Values
GarageCond 0.0555  % of Missing Values
PoolQC 0.9952  % of Missing Values
Fence 0.8075  % of Missing Values
MiscFeature 0.963  % of Missing Values

Now let us plot with the features with missing values vs Sales Price to find some insights about the data¶

In [53]:
for feature in features_with_na:
    dataset = data.copy()
    dataset[feature] = np.where(dataset[feature].isnull(), 1, 0)
    
    # Calculate the mean of SalePrice where the information is missing or present
    dataset.groupby(feature)['SalePrice'].median().plot.bar()
    plt.title(feature)
    plt.show()

Numeric Features¶

In [54]:
total = numeric_features.isnull().sum().sort_values(ascending=False)
percent = (numeric_features.isnull().sum()/numeric_features.isnull().count()).sort_values(ascending=False)
missing_data = pd.concat([total, percent], axis=1,join='outer', keys=['Total Missing Count', '% of Total Observations'])
missing_data.index.name =' Numeric Feature'

missing_data.head(20)
Out[54]:
Total Missing Count % of Total Observations
Numeric Feature
LotFrontage 259 0.177397
GarageYrBlt 81 0.055479
MasVnrArea 8 0.005479
Id 0 0.000000
OpenPorchSF 0 0.000000
KitchenAbvGr 0 0.000000
TotRmsAbvGrd 0 0.000000
Fireplaces 0 0.000000
GarageCars 0 0.000000
GarageArea 0 0.000000
WoodDeckSF 0 0.000000
EnclosedPorch 0 0.000000
HalfBath 0 0.000000
3SsnPorch 0 0.000000
ScreenPorch 0 0.000000
PoolArea 0 0.000000
MiscVal 0 0.000000
MoSold 0 0.000000
YrSold 0 0.000000
BedroomAbvGr 0 0.000000

Missing values for all numeric features in Bar chart Representation¶

In [55]:
missing_values = numeric_features.isnull().sum(axis=0).reset_index()
missing_values.columns = ['column_name', 'missing_count']
missing_values = missing_values.loc[missing_values['missing_count']>0]
missing_values = missing_values.sort_values(by='missing_count')

ind = np.arange(missing_values.shape[0])
width = 0.1
fig, ax = plt.subplots(figsize=(12,3))
rects = ax.barh(ind, missing_values.missing_count.values, color='b')
ax.set_yticks(ind)
ax.set_yticklabels(missing_values.column_name.values, rotation='horizontal')
ax.set_xlabel("Missing Observations Count")
ax.set_title("Missing Observations Count - Numeric Features")
plt.show()

Categorical Features¶

Let us look at the missing values in categorical features in detail

In [56]:
total = categorical_features.isnull().sum().sort_values(ascending=False)
percent = (categorical_features.isnull().sum()/categorical_features.isnull().count()).sort_values(ascending=False)
missing_data = pd.concat([total, percent], axis=1,join='outer', keys=['Total Missing Count', ' % of Total Observations'])
missing_data.index.name ='Feature'
missing_data.head(20)
Out[56]:
Total Missing Count % of Total Observations
Feature
PoolQC 1453 0.995205
MiscFeature 1406 0.963014
Alley 1369 0.937671
Fence 1179 0.807534
FireplaceQu 690 0.472603
GarageType 81 0.055479
GarageCond 81 0.055479
GarageQual 81 0.055479
GarageFinish 81 0.055479
BsmtFinType2 38 0.026027
BsmtExposure 38 0.026027
BsmtFinType1 37 0.025342
BsmtQual 37 0.025342
BsmtCond 37 0.025342
MasVnrType 8 0.005479
Electrical 1 0.000685
Functional 0 0.000000
KitchenQual 0 0.000000
CentralAir 0 0.000000
HeatingQC 0 0.000000

Missing values for Categorical features in Bar chart Representation¶

In [57]:
missing_values = categorical_features.isnull().sum(axis=0).reset_index()
missing_values.columns = ['column_name', 'missing_count']
missing_values = missing_values.loc[missing_values['missing_count']>0]
missing_values = missing_values.sort_values(by='missing_count')

ind = np.arange(missing_values.shape[0])
width = 0.9
fig, ax = plt.subplots(figsize=(12,18))
rects = ax.barh(ind, missing_values.missing_count.values, color='red')
ax.set_yticks(ind)
ax.set_yticklabels(missing_values.column_name.values, rotation='horizontal')
ax.set_xlabel("Missing Observations Count")
ax.set_title("Missing Observations Count - Categorical Features")
plt.show()

Categorical Feature Exploration¶

Let us look at the unique values in categorical features in both train and test dataframes in detail

In [58]:
for column_name in train.columns:
    if train[column_name].dtypes == 'object':
        train[column_name] = train[column_name].fillna(train[column_name].mode().iloc[0])
        unique_category = len(train[column_name].unique())
        print("Feature '{column_name}' has '{unique_category}' unique categories".format(column_name = column_name,
                                                                                         unique_category=unique_category))
 
for column_name in test.columns:
    if test[column_name].dtypes == 'object':
        test[column_name] = test[column_name].fillna(test[column_name].mode().iloc[0])
        unique_category = len(test[column_name].unique())
        print("Features in test set '{column_name}' has '{unique_category}' unique categories".format(column_name = column_name, unique_category=unique_category))

Features in test set 'MSZoning' has '5' unique categories
Features in test set 'Street' has '2' unique categories
Features in test set 'Alley' has '2' unique categories
Features in test set 'LotShape' has '4' unique categories
Features in test set 'LandContour' has '4' unique categories
Features in test set 'Utilities' has '1' unique categories
Features in test set 'LotConfig' has '5' unique categories
Features in test set 'LandSlope' has '3' unique categories
Features in test set 'Neighborhood' has '25' unique categories
Features in test set 'Condition1' has '9' unique categories
Features in test set 'Condition2' has '5' unique categories
Features in test set 'BldgType' has '5' unique categories
Features in test set 'HouseStyle' has '7' unique categories
Features in test set 'RoofStyle' has '6' unique categories
Features in test set 'RoofMatl' has '4' unique categories
Features in test set 'Exterior1st' has '13' unique categories
Features in test set 'Exterior2nd' has '15' unique categories
Features in test set 'MasVnrType' has '4' unique categories
Features in test set 'ExterQual' has '4' unique categories
Features in test set 'ExterCond' has '5' unique categories
Features in test set 'Foundation' has '6' unique categories
Features in test set 'BsmtQual' has '4' unique categories
Features in test set 'BsmtCond' has '4' unique categories
Features in test set 'BsmtExposure' has '4' unique categories
Features in test set 'BsmtFinType1' has '6' unique categories
Features in test set 'BsmtFinType2' has '6' unique categories
Features in test set 'Heating' has '4' unique categories
Features in test set 'HeatingQC' has '5' unique categories
Features in test set 'CentralAir' has '2' unique categories
Features in test set 'Electrical' has '4' unique categories
Features in test set 'KitchenQual' has '4' unique categories
Features in test set 'Functional' has '7' unique categories
Features in test set 'FireplaceQu' has '5' unique categories
Features in test set 'GarageType' has '6' unique categories
Features in test set 'GarageFinish' has '3' unique categories
Features in test set 'GarageQual' has '4' unique categories
Features in test set 'GarageCond' has '5' unique categories
Features in test set 'PavedDrive' has '3' unique categories
Features in test set 'PoolQC' has '2' unique categories
Features in test set 'Fence' has '4' unique categories
Features in test set 'MiscFeature' has '3' unique categories
Features in test set 'SaleType' has '9' unique categories
Features in test set 'SaleCondition' has '6' unique categories

Outliers:¶

In [59]:
for feature in continuous_feature:
    data=train.copy()
    if 0 in data[feature].unique():
        pass
    else:
        data[feature]=np.log(data[feature])
        data.boxplot(column=feature)
        plt.ylabel(feature)
        plt.title(feature)
        plt.show()

Let us find out the relationship between categorical variable and dependent feature SalesPrice¶

In [60]:
for feature in categorical_features:
    data=train.copy()
    data.groupby(feature)['SalePrice'].median().plot.bar()
    plt.xlabel(feature)
    plt.ylabel('SalePrice')
    plt.title(feature)
    plt.show()